MATERIALS, SENSORS, AND MANUFACTURING METHODS FOR NEXT GENERATION OF PERSONAL PROTECTIVE EQUIPMENT
Bibliographic record
Abstract
Air quality including presence of different kinds of harmful chemicals and particles is an important factor for human health. Various designs of protective equipment are currently available commercially mainly focusing on one-time/short-term use in industrial environments based on using microfibrous polypropylene to provide passive protection. During the COVID-19 pandemic, the significant shortage of PPE caused severe problem worldwide. In this thesis, we have developed a new one-piece full head and face respirator with high filtration efficiency, designed to be manufactured without industrial equipment. The environmental impact of PPE has led to interest in using natural polymers for filter materials due to their sustainability and biodegradability. We have developed a compostable zein based air filter produced by electrospinning on a craft paper-based substrate to reducing their environmental impact. The electrospun filter material is tailored to be humidity tolerant and mechanically durable by crosslinking zein with citric acid. We used a folding structure to significantly reduce pressure drop during both single filtration and long-term testing, without compromising other performances. We also aim to develop smart PPEs that can sense the toxic contamination in surroundings and monitor physiological conditions of wearer which is necessary in different circumstances. For detecting harmful substance exposure, we demonstrated a one-step fabrication method for a colorimetric, sensitive, and selective ammonia platform. This sensor was based on simple pH-indicator immobilization electrospun mat with ability to detect concentrations of ammonia as low as 0.5 ppm in a fast response time of 10 sec. We highlighted the durable stability for gaseous and liquid interferences owing to its core-shell nanofiber structure. In other worksites, such as under water or mining industries, the non-availability of medical instruments makes it more important to get real-time monitor of physiological signal to prevent accident. We developed the laser induced graphene-based glucose sensor with one step fabrication on Polycarbonate which is the material of the face shield. The proposed sensor had the sensitivity at 70.1 μA mM-1 cm-2 with the detection range from 0.01 mM to 10 mM. The performance enabled the sensor to be used to monitor glucose level in sweat by implement in the face shield. Overall, we have demonstrated the development of new materials and manufacturing methods for next generation functional PPE. The active sensing function was achieved by two categories of sensor implementation: active protection with toxic gas-ammonia detection, and health monitoring function with glucose sensing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".